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The last decade has witnessed a series of technological advances: social networks, cloud servers, personalized advertising, autonomous cars, personalized healthcare, robotics, security systems, just to name a few. These new technologies have in turn substantially reshaped our demands from adaptive reinforcement learning systems, defining novel yet urgent challenges. In response, a wealth of novel ideas and trends have emerged, tackling problems such as modelling rich and high-dimensional dynamics, life-long learning, resource-bounded planning, and multi-agent cooperation.
The objective of the workshop is to provide a platform for researchers from various areas (e.g., deep learning, game theory, robotics, computational neuroscience, information theory, Bayesian modelling) to disseminate and exchange ideas, evaluating their advantages and caveats. In particular, we will ask participants to address the following questions:
1) What is the future of reinforcement learning?
2) What are the most important challenges?
3) What tools do we need the most?
A final panel discussion will then review the provided answers and focus on elaborating a list of trends and future challenges. Recent advances will be presented in short talks and a poster session based on contributed material.
Author Information
Csaba Szepesvari (University of Alberta)
Marc Deisenroth (Imperial College London)
Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book [Mathematics for Machine Learning](https://mml-book.github.io) published by Cambridge University Press (2020).
Sergey Levine (UC Berkeley)
Pedro Ortega (DeepMind)
Brian Ziebart (University of Illinois at Chicago)
Emma Brunskill (CMU)
Naftali Tishby (The Hebrew University Jerusalem)
Naftali Tishby, is a professor of computer science and the director of the Interdisciplinary Center for Neural Computation (ICNC) at the Hebrew university of Jerusalem. He received his Ph.D. in theoretical physics from the Hebrew University and was a research staff member at MIT and Bell Labs from 1985 to 1991. He was also a visiting professor at Princeton NECI, the University of Pennsylvania and the University of California at Santa Barbara. Dr. Tishby is a leader of machine learning research and computational neuroscience. He was among the first to introduce methods from statistical physics into learning theory, and dynamical systems techniques in speech processing. His current research is at the interface between computer science, statistical physics and computational neuroscience and concerns the foundations of biological information processing and the connections between dynamics and information.
Gerhard Neumann (University of Lincoln)
Daniel Lee (Cornell Tech)
Sridhar Mahadevan (UMass Amherst)
Pieter Abbeel (UC Berkeley & Covariant)
Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter's work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.
David Silver (DeepMind)
Vicenç Gómez (Universitat Pompeu Fabra)
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Pieter Abbeel · John Schulman -
2015 Workshop: Learning, Inference and Control of Multi-Agent Systems »
Vicenç Gómez · Gerhard Neumann · Jonathan S Yedidia · Peter Stone -
2015 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · John Schulman · Satinder Singh · David Silver -
2015 Poster: Online Learning with Gaussian Payoffs and Side Observations »
Yifan Wu · András György · Csaba Szepesvari -
2015 Poster: Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path »
Daniel Hsu · Aryeh Kontorovich · Csaba Szepesvari -
2015 Poster: Linear Multi-Resource Allocation with Semi-Bandit Feedback »
Tor Lattimore · Yacov Crammer · Csaba Szepesvari -
2015 Poster: Softstar: Heuristic-Guided Probabilistic Inference »
Mathew Monfort · Brenden M Lake · Brenden Lake · Brian Ziebart · Patrick Lucey · Josh Tenenbaum -
2015 Poster: Gradient Estimation Using Stochastic Computation Graphs »
John Schulman · Nicolas Heess · Theophane Weber · Pieter Abbeel -
2015 Poster: Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning »
Christoph Dann · Emma Brunskill -
2015 Poster: Learning Continuous Control Policies by Stochastic Value Gradients »
Nicolas Heess · Gregory Wayne · David Silver · Timothy Lillicrap · Tom Erez · Yuval Tassa -
2015 Poster: Combinatorial Cascading Bandits »
Branislav Kveton · Zheng Wen · Azin Ashkan · Csaba Szepesvari -
2015 Poster: Adversarial Prediction Games for Multivariate Losses »
Hong Wang · Wei Xing · Kaiser Asif · Brian Ziebart -
2015 Poster: Model-Based Relative Entropy Stochastic Search »
Abbas Abdolmaleki · Rudolf Lioutikov · Jan Peters · Nuno Lau · Luis Pualo Reis · Gerhard Neumann -
2014 Poster: Universal Option Models »
hengshuai yao · Csaba Szepesvari · Richard Sutton · Joseph Modayil · Shalabh Bhatnagar -
2014 Poster: Robust Classification Under Sample Selection Bias »
Anqi Liu · Brian Ziebart -
2014 Poster: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2014 Poster: Bayes-Adaptive Simulation-based Search with Value Function Approximation »
Arthur Guez · Nicolas Heess · David Silver · Peter Dayan -
2014 Spotlight: Robust Classification Under Sample Selection Bias »
Anqi Liu · Brian Ziebart -
2014 Spotlight: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2013 Workshop: Advances in Machine Learning for Sensorimotor Control »
Thomas Walsh · Alborz Geramifard · Marc Deisenroth · Jonathan How · Jan Peters -
2013 Workshop: Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games. »
Hilbert J Kappen · Naftali Tishby · Jan Peters · Evangelos Theodorou · David H Wolpert · Pedro Ortega -
2013 Poster: Variational Policy Search via Trajectory Optimization »
Sergey Levine · Vladlen Koltun -
2013 Poster: Online Learning with Costly Features and Labels »
Navid Zolghadr · Gábor Bartók · Russell Greiner · András György · Csaba Szepesvari -
2013 Poster: Projected Natural Actor-Critic »
Philip Thomas · William C Dabney · Stephen Giguere · Sridhar Mahadevan -
2013 Poster: Sequential Transfer in Multi-armed Bandit with Finite Set of Models »
Mohammad Gheshlaghi azar · Alessandro Lazaric · Emma Brunskill -
2013 Poster: Optimal Neural Population Codes for High-dimensional Stimulus Variables »
Zhuo Wang · Alan A Stocker · Daniel Lee -
2013 Poster: Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions »
Yasin Abbasi Yadkori · Peter Bartlett · Varun Kanade · Yevgeny Seldin · Csaba Szepesvari -
2012 Workshop: Information in Perception and Action »
Naftali Tishby · Daniel Polani · Tobias Jung -
2012 Poster: Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search »
Arthur Guez · David Silver · Peter Dayan -
2012 Poster: Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss »
Zhuo Wang · Alan A Stocker · Daniel Lee -
2012 Poster: Regularized Off-Policy TD-Learning »
Bo Liu · Sridhar Mahadevan · Ji Liu -
2012 Poster: Near Optimal Chernoff Bounds for Markov Decision Processes »
Teodor Mihai Moldovan · Pieter Abbeel -
2012 Spotlight: Regularized Off-Policy TD-Learning »
Bo Liu · Sridhar Mahadevan · Ji Liu -
2012 Spotlight: Near Optimal Chernoff Bounds for Markov Decision Processes »
Teodor Mihai Moldovan · Pieter Abbeel -
2012 Session: Oral Session 6 »
Csaba Szepesvari -
2012 Poster: Deep Representations and Codes for Image Auto-Annotation »
Jamie Kiros · Csaba Szepesvari -
2012 Poster: Expectation Propagation in Gaussian Process Dynamical Systems »
Marc Deisenroth · Shakir Mohamed -
2012 Poster: Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification »
Yung-Kyun Noh · Frank Park · Daniel Lee -
2012 Poster: A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function »
Pedro Ortega · Tim Genewein · Jordi Grau-Moya · David Balduzzi · Daniel A Braun -
2011 Poster: Improved Algorithms for Linear Stochastic Bandits »
Yasin Abbasi Yadkori · David Pal · Csaba Szepesvari -
2011 Spotlight: Improved Algorithms for Linear Stochastic Bandits »
Yasin Abbasi Yadkori · David Pal · Csaba Szepesvari -
2011 Tutorial: Information Theory in Learning and Control »
Naftali Tishby -
2010 Spotlight: On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient »
Jie Tang · Pieter Abbeel -
2010 Spotlight: Online Markov Decision Processes under Bandit Feedback »
Gergely Neu · András György · András Antos · Csaba Szepesvari -
2010 Poster: Feature Construction for Inverse Reinforcement Learning »
Sergey Levine · Zoran Popovic · Vladlen Koltun -
2010 Poster: Tight Sample Complexity of Large-Margin Learning »
Sivan Sabato · Nati Srebro · Naftali Tishby -
2010 Poster: Learning via Gaussian Herding »
Yacov Crammer · Daniel Lee -
2010 Poster: On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient »
Jie Tang · Pieter Abbeel -
2010 Poster: Online Markov Decision Processes under Bandit Feedback »
Gergely Neu · András György · Csaba Szepesvari · András Antos -
2010 Poster: Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs »
David Pal · Barnabas Poczos · Csaba Szepesvari -
2010 Poster: Generative Local Metric Learning for Nearest Neighbor Classification »
Yung-Kyun Noh · Byoung-Tak Zhang · Daniel Lee -
2010 Poster: Monte-Carlo Planning in Large POMDPs »
David Silver · Joel Veness -
2010 Poster: Parametric Bandits: The Generalized Linear Case »
Sarah Filippi · Olivier Cappé · Aurélien Garivier · Csaba Szepesvari -
2010 Poster: Basis Construction from Power Series Expansions of Value Functions »
Sridhar Mahadevan · Bo Liu -
2010 Poster: Error Propagation for Approximate Policy and Value Iteration »
Amir-massoud Farahmand · Remi Munos · Csaba Szepesvari -
2009 Workshop: Probabilistic Approaches for Control and Robotics »
Marc Deisenroth · Hilbert J Kappen · Emo Todorov · Duy Nguyen-Tuong · Carl Edward Rasmussen · Jan Peters -
2009 Poster: Multi-Step Dyna Planning for Policy Evaluation and Control »
Hengshuai Yao · Richard Sutton · Shalabh Bhatnagar · Dongcui Diao · Csaba Szepesvari -
2009 Poster: Bootstrapping from Game Tree Search »
Joel Veness · David Silver · William Uther · Alan Blair -
2009 Oral: Bootstrapping from Game Tree Search »
Joel Veness · David Silver · William Uther · Alan Blair -
2009 Poster: A General Projection Property for Distribution Families »
Yao-Liang Yu · Yuxi Li · Dale Schuurmans · Csaba Szepesvari -
2009 Poster: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2009 Spotlight: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2008 Workshop: Principled Theoretical Frameworks for the Perception-Action Cycle »
Daniel Polani · Naftali Tishby -
2008 Mini Symposium: Principled Theoretical Frameworks for the Perception-Action Cycle »
Daniel Polani · Naftali Tishby -
2008 Poster: Online Optimization in X-Armed Bandits »
Sebastien Bubeck · Remi Munos · Gilles Stoltz · Csaba Szepesvari -
2008 Poster: Extended Grassmann Kernels for Subspace-Based Learning »
Jihun Hamm · Daniel Lee -
2008 Poster: Regularized Policy Iteration »
Amir-massoud Farahmand · Mohammad Ghavamzadeh · Csaba Szepesvari · Shie Mannor -
2008 Poster: On the Reliability of Clustering Stability in the Large Sample Regime »
Ohad Shamir · Naftali Tishby -
2008 Poster: A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approxi »
Richard Sutton · Csaba Szepesvari · Hamid R Maei -
2008 Spotlight: On the Reliability of Clustering Stability in the Large Sample Regime »
Ohad Shamir · Naftali Tishby -
2007 Oral: Blind channel identification for speech dereverberation using l1-norm sparse learning »
Yuanqing Lin · Jingdong Chen · Youngmoo E Kim · Daniel Lee -
2007 Poster: Blind channel identification for speech dereverberation using l1-norm sparse learning »
Yuanqing Lin · Jingdong Chen · Youngmoo E Kim · Daniel Lee -
2007 Spotlight: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Oral: Cluster Stability for Finite Samples »
Ohad Shamir · Naftali Tishby -
2007 Poster: Cluster Stability for Finite Samples »
Ohad Shamir · Naftali Tishby -
2007 Poster: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion »
J. Zico Kolter · Pieter Abbeel · Andrew Y Ng -
2007 Poster: Fitted Q-iteration in continuous action-space MDPs »
Remi Munos · András Antos · Csaba Szepesvari -
2006 Workshop: Revealing Hidden Elements of Dynamical Systems »
Naftali Tishby -
2006 Poster: Max-margin classification of incomplete data »
Gal Chechik · Geremy Heitz · Gal Elidan · Pieter Abbeel · Daphne Koller -
2006 Spotlight: Max-margin classification of incomplete data »
Gal Chechik · Geremy Heitz · Gal Elidan · Pieter Abbeel · Daphne Koller -
2006 Poster: Information Bottleneck for Non Co-Occurrence Data »
Yevgeny Seldin · Noam Slonim · Naftali Tishby -
2006 Poster: An Application of Reinforcement Learning to Aerobatic Helicopter Flight »
Pieter Abbeel · Adam P Coates · Andrew Y Ng · Morgan Quigley -
2006 Talk: An Application of Reinforcement Learning to Aerobatic Helicopter Flight »
Pieter Abbeel · Adam P Coates · Andrew Y Ng · Morgan Quigley